Autonomous Tracking of Steel Lazy Wave Risers Using a Hybrid Vision–Acoustic AUV Framework

Guardado en:

Detalles Bibliográficos
Publicado en:	Journal of Marine Science and Engineering vol. 13, no. 7 (2025), p. 1347-1365
Autor principal:	Ghasemi, Ali
Otros Autores:	Hodjat, Shiri
Publicado:	MDPI AG
Materias:	Deep water Offshore Digital cameras Unmanned underwater vehicles Inspection Optimization techniques Echo sounding Image processing Tracking Localization Acoustic data Steel structures Ocean floor Autonomous underwater vehicles Steel Risers Echosounders Structural integrity Clustering Cyclic loads Algorithms Offshore production Accuracy Oscillations Deep learning Ocean bottom Underwater robots Unmanned vehicles Robotics Materials fatigue Cables Deepwater drilling Cluster analysis Numerical models Mathematical models Metal fatigue Sensors Hough transformation Underwater vehicles Underwater pipelines Vertical oscillations Fatigue life Simulators Vector quantization Edge detection Environmental
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies.
ISSN:	2077-1312
DOI:	10.3390/jmse13071347
Fuente:	Engineering Database